Dance Movement Recognition and Analysis Based on Deep Learning and Fuzzy Control Algorithms

Abstract

The recognition and analysis of dance movements pose significant challenges due to the complexity and ambiguity of spatiotemporal motion data. This study presents a novel Dance Movement Recognition with Deep Learning and Fuzzy Control (DMR-DFC) framework that integrates Convolutional Neural Networks (CNNs) for spatiotemporal feature extraction and fuzzy logic for interpreting uncertain or overlapping movement patterns. Evaluated on the AIST++ Dance Motion Dataset, the proposed method achieved a classification accuracy of 94.3%, surpassing traditional approaches by 8.5%. Key contributions include: (1) enhanced interpretability through fuzzy rule-based reasoning, (2) improved robustness across varying dance styles and dynamic sequences, and (3) a scalable design suited for real-time applications in automated choreography, virtual reality, and rehabilitation. While the system demonstrates strong performance, it currently relies on high-quality motion capture data, which may limit generalisation in sensor-free environments. Overall, DMR-DFC establishes a powerful and interpretable framework for dance movement recognition that bridges the precision of deep learning with the transparency of fuzzy reasoning.